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@@ -8983,6 +8983,25 @@ The core training code will be integrated into the rag-retrieval library(https:/
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  This work was accomplished during my free time; please grant time a little time.
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  ## Usage
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  ```python
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  ```
 
 
 
 
 
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  ## License
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  **This model should not be used for any commercial purpose!**
 
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  This work was accomplished during my free time; please grant time a little time.
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+ Here's a short introduction to the training method:
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+ The core idea of jasper and stella is distillation: **Let student model learn teacher model's vectors.**
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+ The training process of jasper have 4 stage:
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+ Stage1&2: Distill from teacher vectors. In jasper model the teacher model is nvidia/NV-Embed-v2 and dunzhang/stella_en_1.5B_v5 (Stage1 and Stage2 will freeze different parameters.)
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+ Stage3: MRL training, I made some modifications to MRL to enable training on unsupervised text
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+ Stage4: Alignment between *jasper token embeddings from image's detailed caption* and *vision embeddings from google/siglip-so400m-patch14-384*.
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+ I use a AdaptiveAvgPool2d to do an adjustment on vision tokens' number and dimensions, this method does not need additional parameters.
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+ **The meaning of distillation is to achieve better results with smaller models or as a way of pre-training, not to hit the top of the leaderboards.**
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+ Actually, I've got first place on MTEB (Chinese and English), I will not release the two models, as I said before, it's meaningless.
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  ## Usage
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  ```python
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  ```
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+ ## Evaluation on MTEB
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+ script: ./scripts/evaluate_en_mteb/run_evaluate_mteb.py
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  ## License
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  **This model should not be used for any commercial purpose!**